FATe of Bots: Ethical Considerations of Social Bot Detection

TL;DR

The paper addresses the ethical challenges of social bot detection in large-scale social media by extending the FATe framework (Fairness, Accountability, Transparency) to three core pillars: training data, algorithm development, and bot-agent usage. It employs a mixed-methods approach—a literature snowball survey, multilingual fairness evaluation with BotBuster on OSOME datasets, and qualitative analyses of Reddit user anecdotes and platform policies—to reveal biases in data, uneven application of detection, and lack of transparent communication. Key contributions include operationalizing FATe for social bot detection, empirically demonstrating language and platform biases, and proposing concrete directions for diverse datasets, bias mitigation, and responsible disclosure. These insights aim to foster more trustworthy and equitable bot detection systems capable of preserving platform integrity while protecting user rights. The work has practical implications for researchers, platforms, and policymakers seeking responsible deployment of bot-detection technologies in real-world social ecosystems.

Abstract

A growing suite of research illustrates the negative impact of social media bots in amplifying harmful information with widespread social implications. Social bot detection algorithms have been developed to help identify these bot agents efficiently. While such algorithms can help mitigate the harmful effects of social media bots, they operate within complex socio-technical systems that include users and organizations. As such, ethical considerations are key while developing and deploying these bot detection algorithms, especially at scales as massive as social media ecosystems. In this article, we examine the ethical implications for social bot detection systems through three pillars: training datasets, algorithm development, and the use of bot agents. We do so by surveying the training datasets of existing bot detection algorithms, evaluating existing bot detection datasets, and drawing on discussions of user experiences of people being detected as bots. This examination is grounded in the FATe framework, which examines Fairness, Accountability, and Transparency in consideration of tech ethics. We then elaborate on the challenges that researchers face in addressing ethical issues with bot detection and provide recommendations for research directions. We aim for this preliminary discussion to inspire more responsible and equitable approaches towards improving the social media bot detection landscape.

Figures (1)

• Figure 1: Summary of FATe framework applied to social bot detection in this article, including recommendations for respective research directions.